Comments - Real Time Predictive Models – Are They Possible? - Data Science Central2019-05-25T15:24:54Zhttps://www.datasciencecentral.com/profiles/comment/feed?attachedTo=6448529%3ABlogPost%3A375587&amp%3Bxn_auth=noAs we approach real-time insi…tag:www.datasciencecentral.com,2016-10-05:6448529:Comment:4739952016-10-05T00:53:06.667ZMathieu Landryhttps://www.datasciencecentral.com/profile/MathieuLandry
<p>As we approach real-time insight, the paradox of synchronous present-shaping-future and future-shaping-present becomes philosophically fascinating! ...the birth of AI just around the corner?</p>
<p>As we approach real-time insight, the paradox of synchronous present-shaping-future and future-shaping-present becomes philosophically fascinating! ...the birth of AI just around the corner?</p> Good discussion! I have a co…tag:www.datasciencecentral.com,2016-08-29:6448529:Comment:4634632016-08-29T17:39:23.057ZD. Fullerhttps://www.datasciencecentral.com/profile/DarronFuller
<p>Good discussion! I have a couple of questions: Why wouldn't machine learning techniques based on stochastic gradient descent (SGD) be considered "real-time?" And why wouldn't instance-based methods, such as kNN that does not require global training, be considered real-time predictive? </p>
<p>Good discussion! I have a couple of questions: Why wouldn't machine learning techniques based on stochastic gradient descent (SGD) be considered "real-time?" And why wouldn't instance-based methods, such as kNN that does not require global training, be considered real-time predictive? </p> It is a long and interesting…tag:www.datasciencecentral.com,2016-08-29:6448529:Comment:4630872016-08-29T00:09:16.978ZBoris Shmaginhttps://www.datasciencecentral.com/profile/BorisShmagin
<p>It is a long and interesting post. However, there is no extrapolation in mathematics, only interpolation. Mathematics considered this topics starting from Gödel. </p>
<p><br/>Then cybernetic came, starting from predicting aircraft location as target for an artillery gun, there is a literature on this topic.</p>
<p>I think that some logic may be developed in modern application for business predictions.</p>
<p>It is a long and interesting post. However, there is no extrapolation in mathematics, only interpolation. Mathematics considered this topics starting from Gödel. </p>
<p><br/>Then cybernetic came, starting from predicting aircraft location as target for an artillery gun, there is a literature on this topic.</p>
<p>I think that some logic may be developed in modern application for business predictions.</p> Great article!! really insigh…tag:www.datasciencecentral.com,2016-01-26:6448529:Comment:3776272016-01-26T02:08:52.926ZParker LAUhttps://www.datasciencecentral.com/profile/LeoLi
<p>Great article!! really insightful!</p>
<p>Great article!! really insightful!</p> Dr. Vorhies, thank you for sh…tag:www.datasciencecentral.com,2016-01-25:6448529:Comment:3773812016-01-25T19:49:14.021ZRam Sangireddyhttps://www.datasciencecentral.com/profile/RamSangireddy
<p>Dr. Vorhies, thank you for sharing the insightful information on latest development. However, a few clarifications:</p>
<p></p>
<p>1. As only smaller data sets are being used from small window sizes of real-time data, why even build random forests? Why not build some simpler decision trees that may perform similar or better? That may save some computational overhead?</p>
<p></p>
<p>2. More importantly, it appears that a classification/predictive model is being rebuilt on real-time data for…</p>
<p>Dr. Vorhies, thank you for sharing the insightful information on latest development. However, a few clarifications:</p>
<p></p>
<p>1. As only smaller data sets are being used from small window sizes of real-time data, why even build random forests? Why not build some simpler decision trees that may perform similar or better? That may save some computational overhead?</p>
<p></p>
<p>2. More importantly, it appears that a classification/predictive model is being rebuilt on real-time data for every window; then, what is the new data to be used to score against the model? That is, in practical world, we build the predictive model on historical batch of the data, and then use that model to score the real-time event/transaction data (for example, fraud classification/scoring in credit card transactions in real-time). That leads to my next question:</p>
<p></p>
<p>3. To build the classification/prediction model, the data already has to be labeled with an actual known outcome. How practical is it to assume that the real-time event data is already known with the actual outcome? For same credit card fraud detection example above, while the transaction can be predicted/scored as fraud/not-fraud in real-time, the actual outcome is not known for a while..is it not?</p>